The identification of co-regulated genes is one of the great opportunities for biological research in the genomic era. Although the opportunity is great, the challenges are just as profound. Often existing clustering routines fail to find the main clusters or divide clear clusters. I have developed several new clustering methods, including the hierarchical ordered partitioning and collapsing hybrid (HOPACH), which combines the strengths of both partitioning and agglomerative clustering methods. The Conklin Lab is experienced with experimental biology, bioinformatics, and the development of public bioinformatics software tools (e.g.: GenMAPP, and MAPPFinder). I propose two specific aims for my postdoctoral fellowship: 1. To adapt the HOPACH algorithm for gene expression data analyses. This automated HOPACH algorithm will be written in the R language and will be a contributed package to the R open source statistical software projects. 2. To apply the HOPACH and other analytical tools to a large collection of cardiac and muscle-related gene expression datasets with the goal of developing new visualization methods to analyze gene expression data. The refined gene clusters will be integrated with GenMAPP and other analytical programs so that any biologist can compare expression profile with a larger reference dataset. These studies will begin to define the "topography" of gene expression to help identify new insights into complex biological systems.